The input layer of a NN takes values representative of one local
atomic environment in the form of a feature vector whose dimension
is then equal to the number of its nodes. The feature vector is
built on the basis of Behler-Parrinello (BP) descriptors [1]}
to represent the radial and angular arrangements of atoms in the
local structure using Gaussian symmetry functions having the
translational and rotational invariance. For aluminium, the number
of components of the BP feature was chosen to be 22, comprising
of 12 radial and 10 angular components, as described in more
detail in the Supplementary Information (SI) file. Then, the NN
architecture for aluminum is \(22\times 10\times 10\times 1\)  with, as
a common case, 2 hidden layers of 10 nodes each.
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